{
  "abstract": "Modelled health estimates such as those of the Global Burden of Disease (GBD) study are increasingly used in secondary analyses designed for observed data. However, trajectory clustering typically ignores published uncertainty, selects the number of clusters using criteria blind to it, and assigns countries to groups with unwarranted certainty. We propose an uncertainty-aware framework that separates trajectory level from shape, quantifies membership probabilities by perturbing estimates within their uncertainty intervals, selects clusters based on stability under uncertainty, and examines the roles of development, region, and measurement quality. Applied to age-standardised cardiovascular disease DALY rates for 201 countries from 1990-2023 (GBD 2023), the framework identified two stable trajectory types, whereas the gap statistic selected three. The additional cluster was poorly supported (median membership probability 0.70) and disappeared once uncertainty was considered. The retained clusters had similar burdens in 1990 but diverged markedly over time: a stalled-improvement group of 78 countries (median change $-8\\%$, 40 of them in sub-Saharan Africa) and a sustained-decline group of 92 countries ($-52\\%$). Thirty-one countries remained unclassified because their memberships were uncertain. Results were reproduced using mortality rates, a 2000-2023 analysis window, and bootstrap resampling. Among countries with the narrowest uncertainty intervals, a third well-supported trajectory emerged in former Soviet and Eastern European countries. These findings show that uncertainty affects both the number and composition of trajectory clusters. Ignoring it risks over-interpreting unstable groups and overstating confidence in country classifications. Incorporating published uncertainty and measurement quality should become standard practice when clustering modelled health estimates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.14529",
  "author_total": 1,
  "authors": [
    {
      "name": "Eben Afrifa-Yamoah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/br6a1e6yj05nh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.14529",
  "fulltext_url": null,
  "id": "br6a1e6yj05nh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.14529",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.14529",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/br6a1e6yj05nh.ris",
  "sources": [
    {
      "first_seen": "2026-11-13T06:21:06Z",
      "native_id": "2611.14529",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.14529"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Clustering trajectories of modelled estimates: an uncertainty-aware framework, with an application to global cardiovascular disease burden, 1990 to 2023",
  "title_text": "Clustering trajectories of modelled estimates: an uncertainty-aware framework, with an application to global cardiovascular disease burden, 1990 to 2023",
  "type": "preprint",
  "updated_at": "2026-11-13T06:21:06Z",
  "url": "https://research.draconitic.com/paper/br6a1e6yj05nh",
  "venue": "arXiv",
  "versions": []
}